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Epaminondas Kapetanios
Faculty of Science and Technology
London, United Kingdom
 

Epaminondas Kapetanios has spent many years investigating ways to improve the tasks of interpreting, explaining, and understanding computational artifacts (for example, algorithms, data, knowledge) with a variety of user types and personas. Some notable results of this explorative journey have been the design and implementation of a metadata-driven visual query language, a human-computer interactive automaton and parser for predicting user query intent, and a language and model for adaptive ontologies.

His research culminated in prototypes applicable to a variety of disciplines and real-world projects, including the retrieval of scientific and statistical databases for ozone hole research over the Arctic, natural language-based querying and processing, as well as web-based decision trees as an e-consultation system in healthcare. Epaminondas has also applied web and text mining techniques for competitive business intelligence within the context of an initiative funded by Innovate UK.

Epaminondas is currently affiliated with the School of Physics, Engineering and Computer Science, at the University of Hertfordshire. His work focuses on responsible and trustworthy AI. Specifically, he is investigating human-oriented explainable and interpretable artificial intelligence (AI) and machine learning, such as natural language (conversational, dialogue) based systems and knowledge discovery from source code mining.

Epaminondas has been a reviewer for Computing Reviews since 2014.


     

 The seven tools of causal inference, with reflections on machine learning
Pearl J. Communications of the ACM 62(3): 54-60, 2019.  Type: Article, Reviews: (2 of 3)

This is one of the most influential and eye-opening articles I’ve read in the last two or three years. The author, an ACM Turing Award recipient, makes clear distinctions between machine learning (ML), artificial intelligence...

 

 Understand, manage, and prevent algorithmic bias: a guide for business users and data scientists
Baer T., Apress, New York, NY, 2019. 260 pp.  Type: Book (978-1-484248-84-3)

This is one of the most enlightening books about the hidden risks of applying machine learning techniques, and particularly algorithms, in decision making. The book unveils the many potential sources of algorithmic bias, raising seriou...

 

 ELSA: a multilingual document summarization algorithm based on frequent itemsets and latent semantic analysis
Cagliero L., Garza P., Baralis E. ACM Transactions on Information Systems 37(2): 1-33, 2019.  Type: Article, Reviews: (2 of 2)

“You shall know a word by the company it keeps” is perhaps the most famous quotation attributed to J. R. Firth [1]. Searching for ways to automate natural language understanding (NLU), statistical natural language p...

 

Multidimensional mining of massive text data
Zhang C., Shu K., Morgan&Claypool Publishers, 2019. 198 pp.  Type: Book (978-1-681735-19-1)

This edited book on mining massive text data stands out by putting the concept of a “cube” center stage: a multi-dimensional space in which massive text data analysis should take place. As such, it offers discussion...

 

Mining urban events from the tweet stream through a probabilistic mixture model
Capdevila J., Cerquides J., Torres J. Data Mining and Knowledge Discovery 32(3): 764-786, 2018.  Type: Article

If you are curious about how probabilistic models can be used to analyze tweets for local event detection, this paper is a good start....

 
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